Hello.  I am using metagenomeSeq for a metagenomic dataset.  

I performed two models using fitZig.

Model I, qualitative analysis using two groups (e.g., obesity status: control vs. obesity) as independence variables.

Model II, quantitative analysis using a continuous variable (e.g, BMI) as independence variables.

Additionally, I used some covariates (e.g, age, sex..) in both two models.

I have some questions in the results of MRfulltable after the analyses.

Question 1.

What is the p-value? Is it the model's p-value or the BMI's p-value?

I don't know whoes it is. 

I used 5 variables including 1 independence variable and 4 covariates.

In MRfulltable, I found 5 coefficients, one pvalue, and one adjPvalue.

Is the pvalue the first independence's (obesity status, my interested variable)?

If so, I'm wondering how the fitZig can recognize my interested variable among 5 variables.

Does the fitZig recognize the order of independent variables?

I found the different results when I changed the order of independent variables.

Isn't there p-values about covariate?

If I want to know the p-values of covariates (sex, age...), how can I know them?

Question 2.

I perfomed Model II after class-conversion of BMI using as.numeric(BMI).

I found differences in result table comparing to qualitative analysis (Model I).

The "+samples in group 0" was filled with "0" and "+samples in group 1" was filled with from "0" to "5".

What does it mean?

I don't know why there are groups although Model II is quantitative analysis.

Question 3.

How can I interpret the coefficient?

Is it 2log fold change?

Is it beta coefficient?

I'm sorry but statistics is too difficult for me. T.T

Question 4.

How is the oddsRatio defference from the coefficient?

It's difficult to interpret the MRfulltables.

Question 5.

In Model II, what does the oddsratios mean?

As you know, the continuous variables (BMI) is not categorical group.

Question 6. (Final)

I cannot clearly understand fitZig model yet.

I know that zero-inflated model consists of count-part analysis (only positive counts) and zero-part analysis (zero vs. non-zero).

Which is fitZig, count-part or zero-part?

Sorry, that's a lot of questions.

Many thanks in advance!

Question 1.

See - ?MRfulltable

In the MRfulltable function there are two options:

      by: Column number or column name specifying which coefficient or
          contrast of the linear model is of interest.

    coef: Column number(s) or column name(s) specifying which
          coefficient or contrast of the linear model to display.

By default the second column (by = 2) for displaying p-values, etc.

Question 2.

The # samples, etc. is only meant for the categorical comparisons. 

Question 3.

Same thing - log2 fold change depending on the design.

Question 4.

odds ratio is from the presence/absence test. It's the same as the results of fitPA (fit presence / absence).

Question 5.

It doesn't make sense.

Question 6. (Final)

see http://www.nature.com/nmeth/journal/v10/n12/full/nmeth.2658.html

fitZig fits a zero-inflated gaussian mixture model and estimates weights for the zeros when doing the regression

Edit: Additionally, if you have a non-categorical coefficient you should not be using MRfulltable. That function in particular runs fitPA on your two groups of interest. See ?MRfulltable, ?MRtable, ?MRcoefs for alternative table outputs.